Expert guidance for Django Celery task design, configuration, error handling, and production monitoring.
Works with
Covers task design patterns, Django ORM integration, transaction safety, and idempotency best practices
Includes configuration for brokers, result backends, worker settings, queue routing, and task serialization
Provides error handling strategies: retries with exponential backoff, dead letter queues, timeouts, and exception logging
Supports periodic task scheduling with Celery
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondjango-celery-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches django-celery-expert from vintasoftware/django-ai-plugins and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate django-celery-expert. Access via /django-celery-expert in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
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Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
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Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Identify the task category from the request:
references/django-integration.mdreferences/task-design-patterns.mdreferences/configuration-guide.mdreferences/error-handling.mdreferences/periodic-tasks.mdreferences/monitoring-observability.mdreferences/production-deployment.mdIf the request spans multiple categories, read all relevant reference files before continuing.
Read each reference file identified in Step 1. Do not proceed to implementation without reading the relevant reference.
Apply the patterns from the reference file. Before presenting the solution, verify:
Request: "Send welcome emails in the background after user registration"
# tasks.py
from celery import shared_task
from django.core.mail import send_mail
@shared_task(bind=True, max_retries=3)
def send_welcome_email(self, user_id):
from users.models import User
try:
user = User.objects.get(id=user_id)
send_mail(
subject="Welcome!",
message=f"Hi {user.name}, welcome to our platform!",
from_email="[email protected]",
recipient_list=[user.email],
)
except User.DoesNotExist:
pass
except Exception as exc:
raise self.retry(exc=exc, countdown=60 * (2 ** self.request.retries))
# views.py — queue only after the transaction commits
from django.db import transaction
def register(request):
user = User.objects.create(...)
transaction.on_commit(lambda: send_welcome_email.delay(user.id))
return redirect("dashboard")
Request: "Process a large CSV import with progress updates"
@shared_task(bind=True)
def import_csv(self, file_path, total_rows):
from myapp.models import Record
with open(file_path) as f:
reader = csv.DictReader(f)
for i, row in enumerate(reader):
Record.objects.create(**row)
if i % 100 == 0:
self.update_state(
state="PROGRESS",
meta={"current": i, "total": total_rows},
)
return {"status": "complete", "processed": total_rows}
# Poll progress
result = import_csv.AsyncResult(task_id)
if result.state == "PROGRESS":
progress = result.info.get("current", 0) / result.info.get("total", 1)
Request: "Process an order: validate inventory, charge payment, then send confirmation"
from celery import chain
@shared_task
def validate_inventory(order_id):
order = Order.objects.get(id=order_id)
if not order.items_in_stock():
raise ValueError("Items out of stock")
return order_id
@shared_task
def charge_payment(order_id):
order = Order.objects.get(id=order_id)
order.charge()
return order_id
@shared_task
def send_confirmation(order_id):
Order.objects.get(id=order_id).send_confirmation_email()
def process_order(order_id):
chain(
validate_inventory.s(order_id),
charge_payment.s(),
send_confirmation.s(),
).delay()
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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django-celery-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: django-celery-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in django-celery-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
django-celery-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend django-celery-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: django-celery-expert is focused, and the summary matches what you get after install.
django-celery-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added django-celery-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
django-celery-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
django-celery-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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